{
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   "cell_type": "markdown",
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   "source": [
    "# 3. Linear Regression – Conceptual\n",
    "\n",
    "Excercises from **Chapter 3** of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.\n",
    "\n",
    "I've elected to use Python instead of R."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![table3.4](./images/3_table3.4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q1.** Describe the null hypotheses to which the p-values given in Table 3.4 correspond. Explain what conclusions you can draw based on these p-values. Your explanation should be phrased in terms of sales, TV, radio, and newspaper, rather than in terms of the coefficients of the linear model.\n",
    "\n",
    "**A.** The null hypotheses are  \n",
    "\n",
    "1. There is no relationship between amount spent on TV advertising and Sales \n",
    "2. There is no relationship between amount spent on radio ads and Sales\n",
    "3. There is no relationship between amount spent on newspaper ads and Sales\n",
    "\n",
    "The p-values given in table 3.4 suggest that we can reject the null hypotheses 1 & 2, it seems likely that there is a relationship between TV ads and Sales, and radio ads and sales.\n",
    "\n",
    "The p-value associated with the t-statistic for newspaper ads is high which suggests that we *cannot* reject null hypothesis 3. This suggests that there is no significant relationship between newspaper ads and sales.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q2.** Carefully explain the differences between the KNN classifier and KNN regression methods.\n",
    "\n",
    "**A.** The KNN classifier and the KNN regression methods are largely similar. The KNN classifier determines a decision boundary which can be used to segment data into 2 or more clusters or groups. KNN regression is non-parmetric method for estimating a regression function that can be used to predict some quantitivie variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q3.** Suppose we have a data set with five predictors, X1 = GPA, X2 = IQ, X3 = Gender (1 for Female and 0 for Male), X4 = Interaction between GPA and IQ, and X5 = Interaction between GPA and Gender. The response is starting salary after graduation (in thousands of dollars). Suppose we use least squares to fit the model, and get β_0 = 50, β_1 = 20 , β_2 = 0.07 , β_3 = 35 , β_4 = 0.01 , β_5 = −10 .\n",
    "\n",
    "**(a)** Which answer is correct, and why?\n",
    "\n",
    "- i. For a fixed value of IQ and GPA, males earn more on average than females.\n",
    "- ii. For a fixed value of IQ and GPA, females earn more on average than males.\n",
    "- iii. For a fixed value of IQ and GPA, males earn more on average than females provided that the GPA is high enough.\n",
    "- iv. For a fixed value of IQ and GPA, females earn more on average than males provided that the GPA is high enough.\n",
    "    \n",
    "**A.** iii. For a fixed value of IQ and GPA, males earn more on average than females provided that the GPA is high enough. \n",
    "\n",
    "Because X3 is our dummy variable for gender with 1 for female and 0 male, and coefficient 35, which means – all else being equal – the model will estimate a starting salary for females $35k higher than for males, but there is an additional interaction variable concerning GPA and gender which means if GPA > 3.5 then males earn more than females.\n",
    "\n",
    "**(b)** Predict the salary of a female with IQ of 110 and a GPA of 4.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$137100.0\n"
     ]
    }
   ],
   "source": [
    "def f(gpa, iq, gender):\n",
    "    return 50 + 20*gpa + 0.07*iq + 35*gender + 0.01*gpa*iq + (-10*gpa*gender)\n",
    "\n",
    "gpa = 4\n",
    "iq  = 110\n",
    "gender = 1\n",
    "\n",
    "print('$' + str(f(gpa, iq, gender) * 1000)) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(c) True or false: Since the coefficient for the GPA/IQ interaction term is very small, there is very little evidence of an interaction effect. Justify your answer.\n",
    "\n",
    "False: the interaction effect might be small but we would need to inspect the standard error to understand if this interaction effect is significant. If the standard error is also very small then it might still be considered a significant effect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q4.** I collect a set of data (n = 100 observations) containing a single predictor and a quantitative response. I then fit a linear regression model to the data, as well as a separate cubic regression, i.e.  \n",
    "Y = β0 + β1X + β2X^2 + β3X^3 + ε\n",
    "\n",
    "**(a)** Suppose that the true relationship between X and Y is linear, i.e. Y = β0 + β1X + ε. Consider the training residual sum of squares (RSS) for the linear regression, and also the training RSS for the cubic regression. Would we expect one to be lower than the other, would we expect them to be the same, or is there not enough information to tell? Justify your answer.\n",
    "\n",
    "**A:** \n",
    "We would expect the training RSS for the cubic model because it is more flexible which allows it to fit more closely variance in the training data – which will reduce RSS despite this note being representative of a closer approaximation to the true linear relationship that is f(x).\n",
    "\n",
    "**(b)** Answer (a) using test rather than training RSS.\n",
    "\n",
    "**A:**  We would expect the test RSS for the linear regression to be lower because the assumption of high bias is correct and so the lack of flexibility in that model is of no cost in estimating the true f(x). The cubic model is more flexible, and so is likely to overfit the training data meaning that the fit of the model will be affected by variance in the training data that is not representive of the true f(x).\n",
    "\n",
    "**(c)** Suppose that the true relationship between X and Y is not linear, but we don’t know how far it is from linear. Consider the training RSS for the linear regression, and also the training RSS for the cubic regression. Would we expect one to be lower than the other, would we expect them to be the same, or is there not enough information to tell? Justify your answer.\n",
    "\n",
    "**A:** We expect training RSS to decrease as the the variance/flexibility of our model increases. This holds true regardles of the true value of f(x). So we expect the cubic model to result in a lower training RSS\n",
    "\n",
    "**(d)** Answer (c) using test rather than training RSS.\n",
    "\n",
    "There is not enough information to answer this fully.\n",
    "\n",
    "If the true relationship is highly non-linear and there is low noise (or irreducible error) in our training data then we might expect the more flexible cubic model to deliver a better test RSS.\n",
    "\n",
    "However, if the relationship is only slightly non-linear or the noise in our training data is high then a linear model might deliver better results."
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5.** Consider the fitted values that result from performing linear regression without an intercept. In this setting, the i-th fitted value takes the form:\n",
    "\n",
    "![Screen%20Shot%202018-09-06%20at%2018.57.02.png](attachment:Screen%20Shot%202018-09-06%20at%2018.57.02.png)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is a_i′ ?\n",
    "\n",
    "*Note: We interpret this result by saying that the fitted values from linear regression are linear combinations of the response values.*\n",
    "\n",
    "![IMG_1922.jpg](./images/3_5.jpg)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6.** Using (3.4), argue that in the case of simple linear regression, the least squares line always passes through the point (x ̄, y ̄).\n",
    "\n",
    "![IMG_1923.jpg](./images/3_6.jpg)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**7.** It is claimed in the text that in the case of simple linear regression of Y onto X, the R2 statistic (3.17) is equal to the square of the correlation between X and Y (3.18). Prove that this is the case. For simplicity, you may assume that x ̄ = y ̄ = 0.\n",
    "\n",
    "![IMG_1924.jpg](./images/3_7.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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    "version": 3
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